Monday, July 3, 2017

The effects of mutational process and selection on driver mutations across cancer types

New Results

The effects of mutational process and selection on driver mutations across cancer types

Daniel TemkoIan TomlinsonSimone SeveriniBenjamin Schuster-BoecklerTrevor Graham

Abstract

Epidemiological evidence has long associated environmental mutagens with increased cancer risk. However, links between specific mutation-causing processes and the acquisition of individual driver mutations have remained obscure. Here we have used public cancer sequencing data to infer the independent effects of mutation and selection on driver mutation complement. First, we detect associations between a range of mutational processes, including those linked to smoking, ageing, APOBEC and DNA mismatch repair (MMR) and the presence of key driver mutations across cancer types. Second, we quantify differential selection between well-known alternative driver mutations, including differences in selection between distinct mutant residues in the same gene. These results show that while mutational processes play a large role in determining which driver mutations are present in a cancer, the role of selection frequently dominates.

http://www.biorxiv.org/content/early/2017/06/12/149096

Thursday, May 11, 2017

Extinction Times In Tumor Public Goods Games

Extinction Times In Tumor Public Goods Games

Philip GerleePhilipp M. Altrock

Abstract

Cancer evolution and progression are shaped by Darwinian selection and cell-to-cell interactions. Evolutionary game theory incorporates both of these principles, and has been recently as a framework to describe tumor cell population dynamics. A cornerstone of evolutionary dynamics is the replicator equation, which describes changes in the relative abundance of different cell types, and is able to predict evolutionary equilibria. Typically, the replicator equation focuses on differences in relative fitness. We here show that this framework might not be sufficient under all circumstances, as it neglects important aspects of population growth. Standard replicator dynamics might miss critical differences in the time it takes to reach an equilibrium, as this time also depends on cellular birth and death rates in growing but bounded populations. As the system reaches a stable manifold, the time to reach equilibrium depends on cellular death and birth rates. These rates shape evolutionary timescales, in particular in competitive co-evolutionary dynamics of growth factor producers and free-riders. Replicator dynamics might be an appropriate framework only when birth and death rates are of comparable magnitude. Otherwise, population growth effects cannot be neglected when predicting the time to reach an equilibrium, and cellular events have to be accounted for explicitly.

http://biorxiv.org/content/early/2017/05/04/134361

Wednesday, May 10, 2017

Mechanistic Modeling Quantifies The Influence Of Tumor Growth Kinetics On The Response To Anti-Angiogenic Treatment

Mechanistic Modeling Quantifies The Influence Of Tumor Growth Kinetics On The Response To Anti-Angiogenic Treatment

Thomas D. Gaddy, Stacey D. Finley

Abstract

Tumors exploit angiogenesis, the formation of new blood vessels from pre-existing vasculature, in order to obtain nutrients required for continued growth and proliferation. Targeting factors that regulate angiogenesis, including the potent promoter vascular endothelial growth factor (VEGF), is therefore an attractive strategy for inhibiting tumor growth. Systems biology modeling enables us to identify tumor-specific properties that influence the response to those anti-angiogenic strategies. Here, we build on our previous systems biology model of VEGF transport and kinetics in tumor-bearing mice to include a tumor compartment whose volume depends on the “angiogenic signal” produced when VEGF binds to its receptors on tumor endothelial cells. We trained and validated the model using in vivo measurements of xenograft tumor volume to produce a model that accurately predicts the tumor's response to anti-angiogenic treatment. We applied the model to investigate how tumor growth kinetics influence the response to anti-angiogenic treatment targeting VEGF. Based on multivariate regression analysis, we found that certain intrinsic kinetic parameters that characterize the growth of tumors could successfully predict response to anti-VEGF treatment. This model is a useful tool for predicting which tumors will respond to anti-VEGF treatment, complementing pre-clinical in vivo studies.

Tuesday, March 14, 2017

Stochastic model of contact inhibition and the proliferation of melanoma in situ

Stochastic model of contact inhibition and the proliferation of melanoma in situ

Mauro Cesar C MoraisIzabella StuhlAlan U SabinoWillian W LautenschlagerAlexandre S QueirogaTharcisio C TortelliRoger ChammasYuri SuhovAlexandre F Ramos

Abstract

Contact inhibition is a central feature orchestrating cell proliferation in culture experiments with its loss being associated with malignant transformation and tumorigenesis. We performed a co-culture experiment with human metastatic melanoma cell line (SK-MEL-147) and immortalized keratinocyte cells (HaCaT). After 8 days a spatial pattern was detected, characterized by the formation of clusters of melanoma cells surrounded by keratinocytes constraining their proliferation. In addition, we observed that the proportion of melanoma cells within the total population has increased. To explain our results we propose a spatial stochastic model (following a philosophy of the Widom-Rowlinson model from Statistical Physics and Molecular Chemistry) where we consider cell proliferation, death, migration, and cell-to-cell interaction through contact inhibition. Our numerical simulations demonstrate that loss of contact inhibition is a sufficient mechanism, appropriate for an explanation of the increase in the proportion of tumor cells and generation of spatial patterns established in conducted experiments.
http://biorxiv.org/content/early/2017/03/02/110007

A cautionary tale on using tumour growth rate to predict survival

A cautionary tale on using tumour growth rate to predict survival

Hitesh MistryFernando Ortega

Abstract

A recurrent question within oncology drug development is predicting phase III outcome for a new treatment using early clinical data. One approach to tackle this problem has been to derive metrics from mathematical models that describe tumour size dynamics termed re-growth rate and time to tumour re-growth. They have shown to be strong predictors of overall survival in numerous studies but there is debate about how these metrics are derived and if they are more predictive than empirical end-points. This work explores the issues raised in using model-derived metric as predictors for survival analyses. Re-growth rate and time to tumour re-growth were calculated for three large clinical studies by forward and reverse alignment. The latter involves re-aligning patients to their time of progression. Hence it accounts for the time taken to estimate re-growth rate and time to tumour re-growth but also assesses if these predictors correlate to survival from the time of progression. We found that neither re-growth rate nor time to tumour re-growth correlated to survival using reverse alignment. This suggests that the dynamics of tumours up until disease progression has no relationship to survival post progression. For prediction of a phase III trial we found the metrics performed no better than empirical end-points. These results highlight that care must be taken when relating dynamics of tumour imaging to survival and that bench-marking new approaches to existing ones is essential.

http://biorxiv.org/content/early/2017/02/20/109934